\IEEEcompsoctitleabstractindextext{%
\begin{abstract}
%In many big data applications, it is important to survey and explore text streams with many hierarchical and evolving topics.
%\kg{Surveying and exploring text streams with} many hierarchical and evolving topics \kg{is important in many big data applications.}
%A core challenge is to connect big data with people, that is, to present interesting and evolving topics effectively to humans in an understandable and manageable manner.
%A core challenge is to connect big data with people, that is, to present interesting and evolving topics effectively in an understandable and manageable manner.
%In this paper, we report a concrete progress in this area.
%\kg{This paper reports} a concrete progress in this area.
%Technically, we learn a set of evolutionary tree cuts from the topic trees based on user-selected focus nodes.
We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams.
%\dc{This paper introduces} an online visual analytics approach \dc{for} helping users explore and understand hierarchical topic evolution in high-volume text streams.
%The key idea behind this approach is to identify representative topics in the incoming documents and align them with the existing ones.
%The key idea behind this approach is to identify representative topics in the incoming documents and align them with existing representative \dc{topics} that they immediately follow along time.
%The key idea behind this approach is to identify representative topics in the incoming documents and align them with the existing representative topics that they immediately follow (in time).
The key idea behind this approach is to identify representative topics \docpr{in incoming} documents and align them with the existing representative topics that they immediately follow (in time).
To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes.
%A posterior probability distribution is adopted to derive the optimal tree cuts in the tree(s) that the focus nodes belong to (seed tree cut).
%A posterior probability distribution is adopted to derive the optimal tree cut(s) in the tree(s) that the focus nodes belong to (seed tree cut).
%The hidden Markov model is developed to propagate the seed tree cuts to the new coming topic trees with the goal of balancing the fitness of each tree cut and the smoothness between adjacent tree cuts.
%The hidden Markov model is developed to propagate the seed tree cuts to the new coming topic trees \kg{to balance} the fitness of each tree cut and the smoothness between adjacent tree cuts.
%A dynamic Bayesian network model is developed to derive the tree cuts in the \dc{incoming} topic trees \kg{to balance} the fitness of each tree cut and the smoothness between adjacent tree cuts.
A dynamic Bayesian network model \docpr{has been} developed to derive the tree cuts in the \dc{incoming} topic trees \kg{to balance} the fitness of each tree cut and the smoothness between adjacent tree cuts.
%By connecting the corresponding topics at different times, we provide an overview of the evolving hierarchical topics.
By connecting the corresponding topics at different times, we \docpr{are able to} provide an overview of the evolving hierarchical topics.
%\kg{We} provide an overview of the evolving hierarchical topics \kg{by connecting the corresponding topics at different times.}
%A sedimentation-based visualization is designed to enable the interactive analysis of streaming text data from global patterns to local details.
A sedimentation-based visualization \dc{has been} designed to enable the interactive analysis of streaming text data from global patterns to local details.
%We evaluate our method on real-world news datasets and report the highly promising results.\looseness=-1
%We evaluate our method on real-world datasets and the results are generally favorable.
We \docpr{evaluated} our method on real-world datasets and the results are generally favorable.

%\dc{are promising}.\looseness=-1
%evolutionary topic trees are built from the different time windows of a text stream in a Bayesian online filtering framework.
%the results have been promising, especially in helping to analyze evolving hierarchical topics in text data.

\end{abstract}

\begin{keywords}
streaming text data, evolutionary tree clustering, streaming tree cut, streaming topic visualization.
\end{keywords}
} 